Distributed Offline Data Reconstruction in BaBar

TL;DR

This paper describes the development and deployment of a distributed, two-pass data reconstruction system for the BaBar experiment, enabling efficient processing and off-site distribution of large-scale data with high performance.

Contribution

It introduces a novel two-pass, distributed processing architecture for large-scale data reconstruction, including off-site processing and system performance details.

Findings

Successfully processed 90 TB dataset from SLAC

Achieved processing of 2-4 million events per day on distributed farms

Demonstrated effective off-site data processing and quality control

Abstract

The BaBar experiment at SLAC is in its fourth year of running. The data processing system has been continuously evolving to meet the challenges of higher luminosity running and the increasing bulk of data to re-process each year. To meet these goals a two-pass processing architecture has been adopted, where 'rolling calibrations' are quickly calculated on a small fraction of the events in the first pass and the bulk data reconstruction done in the second. This allows for quick detector feedback in the first pass and allows for the parallelization of the second pass over two or more separate farms. This two-pass system allows also for distribution of processing farms off-site. The first such site has been setup at INFN Padova. The challenges met here were many. The software was ported to a full Linux-based, commodity hardware system. The raw dataset, 90 TB, was imported from SLAC utilizing a 155 Mbps network link. A system for quality control and export of the processed data back to SLAC was developed. Between SLAC and Padova we are currently running three pass-one farms, with 32 CPUs each, and nine pass-two farms with 64 to 80 CPUs each. The pass-two farms can process between 2 and 4 million events per day. Details about the implementation and performance of the system will be presented.